blaz-r/OSCD_RGB_Cropped_96

Onera Satellite Change Detection Dataset - RGB - Cropped to 96x96 patches

The Onera Satellite Change Detection dataset addresses the issue of detecting changes between satellite images from different dates.

It comprises 24 pairs of multispectral images taken from the Sentinel-2 satellites between 2015 and 2018. Locations are picked all over the world, in Brazil, USA, Europe, Middle-East and Asia. For each location, registered pairs of 13-band multispectral satellite images obtained by the Sentinel-2 satellites are provided. Images vary in spatial resolution between 10m, 20m and 60m.

Pixel-level change ground truth is provided for all 14 training and 10 test image pairs. The annotated changes focus on urban changes, such as new buildings or new roads. These data can be used for training and setting parameters of change detection algorithms.

Dataset Details

Dataset Description

The images were cropped to 96x96 patches to enable direct usage with the standard protocol.

• License: CC BY-NC-SA

Dataset Sources

• Homepage: https://rcdaudt.github.io/oscd/
• Paper: ArXiv

Uses

from datasets import load_dataset

oscd96 = load_dataset("blaz-r/OSCD_RGB_Cropped_96")

>>> oscd96["train"]
# single "sample" with 3 features: "imageA", "imageB", "label"

Citation

If you use this work for your projects, please take the time to cite the original paper:

@inproceedings{daudt2018urban,
  title={Urban change detection for multispectral earth observation using convolutional neural networks},
  author={Daudt, Rodrigo Caye and Le Saux, Bertr and Boulch, Alexandre and Gousseau, Yann},
  booktitle={IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium},
  pages={2115--2118},
  year={2018},
  organization={IEEE}
}